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<li><a class="reference internal" href="#">Image tutorial</a><ul>

<li><a class="reference internal" href="#startup-commands">Startup commands</a></li>

<li><a class="reference internal" href="#importing-image-data-into-numpy-arrays">Importing image data into Numpy arrays</a></li>

<li><a class="reference internal" href="#plotting-numpy-arrays-as-images">Plotting numpy arrays as images</a><ul>

<li><a class="reference internal" href="#applying-pseudocolor-schemes-to-image-plots">Applying pseudocolor schemes to image plots</a></li>

<li><a class="reference internal" href="#color-scale-reference">Color scale reference</a></li>

<li><a class="reference internal" href="#examining-a-specific-data-range">Examining a specific data range</a></li>

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<span id="id1"></span><h1>Image tutorial<a class="headerlink" href="#image-tutorial" title="Permalink to this headline">¶</a></h1>

<span id="imaging-startup"></span><h2>Startup commands<a class="headerlink" href="#startup-commands" title="Permalink to this headline">¶</a></h2>

<p>At the very least, you’ll need to have access to the

<a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><tt class="xref py py-func docutils literal"><span class="pre">imshow()</span></tt></a> function. There are a couple of

ways to do it. The easy way for an interactive environment:</p>

<div class="highlight-python"><pre>$ipython -pylab</pre>

</div>

<p>The imshow function is now directly accessible (it’s in your

<a class="reference external" href="http://bytebaker.com/2008/07/30/python-namespaces/">namespace</a>).

See also <a class="reference internal" href="pyplot_tutorial.html#pyplot-tutorial"><em>Pyplot tutorial</em></a>.</p>

<p>The more expressive, easier to understand later method (use this in

your scripts to make it easier for others (including your future self)

to read) is to use the matplotlib API (see <a class="reference internal" href="artists.html#artist-tutorial"><em>Artist tutorial</em></a>)

where you use explicit namespaces and control object creation, etc...</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [1]: </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>

<span class="gp">In [2]: </span><span class="kn">import</span> <span class="nn">matplotlib.image</span> <span class="kn">as</span> <span class="nn">mpimg</span>

<span class="gp">In [3]: </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>

</pre></div>

</div>

<p>Examples below will use the latter method, for clarity. In these

examples, if you use the -pylab method, you can skip the “mpimg.” and

“plt.” prefixes.</p>

</div>

<span id="importing-data"></span><h2>Importing image data into Numpy arrays<a class="headerlink" href="#importing-image-data-into-numpy-arrays" title="Permalink to this headline">¶</a></h2>

<p>Plotting image data is supported by the Python Image Library (<a class="reference external" href="http://www.pythonware.com/products/pil/">PIL</a>). Natively, matplotlib

only supports PNG images. The commands shown below fall back on PIL

if the native read fails.</p>

<p>The image used in this example is a PNG file, but keep that PIL

requirement in mind for your own data.</p>

<p>Here’s the image we’re going to play with:</p>

<p>It’s a 24-bit RGB PNG image (8 bits for each of R, G, B). Depending

on where you get your data, the other kinds of image that you’ll most

likely encounter are RGBA images, which allow for transparency, or

single-channel grayscale (luminosity) images. You can right click on

it and choose “Save image as” to download it to your computer for the

rest of this tutorial.</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [4]: </span><span class="n">img</span><span class="o">=</span><span class="n">mpimg</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s">'stinkbug.png'</span><span class="p">)</span>

<span class="go">array([[[ 0.40784314, 0.40784314, 0.40784314],</span>

<span class="go"> [ 0.44313726, 0.44313726, 0.44313726]]], dtype=float32)</span>

</pre></div>

</div>

<p>Note the dtype there - float32. Matplotlib has rescaled the 8 bit

data from each channel to floating point data between 0.0 and 1.0. As

a side note, the only datatype that PIL can work with is uint8.

Matplotlib plotting can handle float32 and uint8, but image

reading/writing for any format other than PNG is limited to uint8

data. Why 8 bits? Most displays can only render 8 bits per channel

worth of color gradation. Why can they only render 8 bits/channel?

Because that’s about all the human eye can see. More here (from a

photography standpoint): <a class="reference external" href="http://www.luminous-landscape.com/tutorials/bit-depth.shtml">Luminous Landscape bit depth tutorial</a>.</p>

<p>Each inner list represents a pixel. Here, with an RGB image, there

are 3 values. Since it’s a black and white image, R, G, and B are all

similar. An RGBA (where A is alpha, or transparency), has 4 values

per inner list, and a simple luminance image just has one value (and

is thus only a 2-D array, not a 3-D array). For RGB and RGBA images,

matplotlib supports float32 and uint8 data types. For grayscale,

matplotlib supports only float32. If your array data does not meet

one of these descriptions, you need to rescale it.</p>

</div>

<span id="plotting-data"></span><h2>Plotting numpy arrays as images<a class="headerlink" href="#plotting-numpy-arrays-as-images" title="Permalink to this headline">¶</a></h2>

<p>So, you have your data in a numpy array (either by importing it, or by

generating it). Let’s render it. In Matplotlib, this is performed

using the <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><tt class="xref py py-func docutils literal"><span class="pre">imshow()</span></tt></a> function. Here we’ll grab

the plot object. This object gives you an easy way to manipulate the

plot from the prompt.</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [5]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-1.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-1.png">png</a>, <a class="reference external" href="../users/image_tutorial-1.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-1.pdf">pdf</a>)</p>

</div>

<p>You can also plot any numpy array - just remember that the datatype

must be float32 (and range from 0.0 to 1.0) or uint8.</p>

<span id="pseudocolor"></span><h3>Applying pseudocolor schemes to image plots<a class="headerlink" href="#applying-pseudocolor-schemes-to-image-plots" title="Permalink to this headline">¶</a></h3>

<p>Pseudocolor can be a useful tool for enhancing contrast and

visualizing your data more easily. This is especially useful when

making presentations of your data using projectors - their contrast is

typically quite poor.</p>

<p>Pseudocolor is only relevant to single-channel, grayscale, luminosity

images. We currently have an RGB image. Since R, G, and B are all

similar (see for yourself above or in your data), we can just pick one

channel of our data:</p>

</pre></div>

</div>

<p>This is array slicing. You can read more in the <a class="reference external" href="http://www.scipy.org/Tentative_NumPy_Tutorial">Numpy tutorial</a>.</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [7]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-2.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-2.png">png</a>, <a class="reference external" href="../users/image_tutorial-2.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-2.pdf">pdf</a>)</p>

</div>

<p>Now, with a luminosity image, the default colormap (aka lookup table,

LUT), is applied. The default is called jet. There are plenty of

others to choose from. Let’s set some others using the

<tt class="xref py py-meth docutils literal"><span class="pre">set_cmap()</span></tt> method on our image plot

object:</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [8]: </span><span class="n">imgplot</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="s">'hot'</span><span class="p">)</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-3.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-3.png">png</a>, <a class="reference external" href="../users/image_tutorial-3.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-3.pdf">pdf</a>)</p>

</div>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [9]: </span><span class="n">imgplot</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="s">'spectral'</span><span class="p">)</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-4.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-4.png">png</a>, <a class="reference external" href="../users/image_tutorial-4.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-4.pdf">pdf</a>)</p>

</div>

<p>There are many other colormap schemes available. See the <a class="reference external" href="http://matplotlib.org/examples/color/colormaps_reference.html">list and

images of the colormaps</a>.</p>

</div>

<span id="color-bars"></span><h3>Color scale reference<a class="headerlink" href="#color-scale-reference" title="Permalink to this headline">¶</a></h3>

<p>It’s helpful to have an idea of what value a color represents. We can

do that by adding color bars. It’s as easy as one line:</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [10]: </span><span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-5.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-5.png">png</a>, <a class="reference external" href="../users/image_tutorial-5.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-5.pdf">pdf</a>)</p>

</div>

<p>This adds a colorbar to your existing figure. This won’t

automatically change if you change you switch to a different

colormap - you have to re-create your plot, and add in the colorbar

again.</p>

</div>

<span id="data-ranges"></span><h3>Examining a specific data range<a class="headerlink" href="#examining-a-specific-data-range" title="Permalink to this headline">¶</a></h3>

<p>Sometimes you want to enhance the contrast in your image, or expand

the contrast in a particular region while sacrificing the detail in

colors that don’t vary much, or don’t matter. A good tool to find

interesting regions is the histogram. To create a histogram of our

image data, we use the <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist"><tt class="xref py py-func docutils literal"><span class="pre">hist()</span></tt></a> function.</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="go">In[10]: plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='k', ec='k')</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-6.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-6.png">png</a>, <a class="reference external" href="../users/image_tutorial-6.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-6.pdf">pdf</a>)</p>

</div>

<p>Most often, the “interesting” part of the image is around the peak,

and you can get extra contrast by clipping the regions above and/or

below the peak. In our histogram, it looks like there’s not much

useful information in the high end (not many white things in the

image). Let’s adjust the upper limit, so that we effectively “zoom in

on” part of the histogram. We do this by calling the

<tt class="xref py py-meth docutils literal"><span class="pre">set_clim()</span></tt> method of the image plot

object.</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="go">In[11]: imgplot.set_clim(0.0,0.7)</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-7.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-7.png">png</a>, <a class="reference external" href="../users/image_tutorial-7.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-7.pdf">pdf</a>)</p>

</div>

<span id="interpolation"></span><h3>Array Interpolation schemes<a class="headerlink" href="#array-interpolation-schemes" title="Permalink to this headline">¶</a></h3>

<p>Interpolation calculates what the color or value of a pixel “should”

be, according to different mathematical schemes. One common place

that this happens is when you resize an image. The number of pixels

change, but you want the same information. Since pixels are discrete,

there’s missing space. Interpolation is how you fill that space.

This is why your images sometimes come out looking pixelated when you

blow them up. The effect is more pronounced when the difference

between the original image and the expanded image is greater. Let’s

take our image and shrink it. We’re effectively discarding pixels,

only keeping a select few. Now when we plot it, that data gets blown

up to the size on your screen. The old pixels aren’t there anymore,

and the computer has to draw in pixels to fill that space.</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [8]: </span><span class="kn">import</span> <span class="nn">Image</span>

<span class="gp">In [9]: </span><span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s">'stinkbug.png'</span><span class="p">)</span> <span class="c"># Open image as PIL image object</span>

<span class="gp">In [10]: </span><span class="n">rsize</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">10</span><span class="p">,</span><span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="mi">10</span><span class="p">))</span> <span class="c"># Use PIL to resize</span>

<span class="gp">In [11]: </span><span class="n">rsizeArr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">rsize</span><span class="p">)</span> <span class="c"># Get array back</span>

<span class="gp">In [12]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">rsizeArr</span><span class="p">)</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-8.py">Source code</a>)</p>

<p>Here we have the default interpolation, bilinear, since we did not

give <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><tt class="xref py py-func docutils literal"><span class="pre">imshow()</span></tt></a> any interpolation argument.</p>

<p>Let’s try some others:</p>

<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [10]: </span><span class="n">imgplot</span><span class="o">.</span><span class="n">set_interpolation</span><span class="p">(</span><span class="s">'nearest'</span><span class="p">)</span>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-9.py">Source code</a>)</p>

</pre></div>

</div>

<p>(<a class="reference external" href="../users/image_tutorial-10.py">Source code</a>)</p>

<p>Bicubic interpolation is often used when blowing up photos - people

tend to prefer blurry over pixelated.</p>

</div>

</div>

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